• Journal of Digital Convergence
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• v.20 no.2
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• pp.367-373
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• 2022

The purpose of this study is to derive the factors for purchasing a meal kit in their 20s and 30s and analyze the purchasing behavior from which factors they want to buy a meal kit in each lifestyle type. The first methodology of this study is inducing 7 factors derived from previous research on purchasing a meal kit. The second is the in-depth interview on 3 male and 3 female participants with clear purchasing criteria. As a result of the study, meal kit buyers in their 20s-30s evaluated the importance of purchasing factors in the order of quality, convenience, and taste on average in the survey. In in-depth interviews, more than half answered that they could be satisfied with the experience of using the meal kit at least freshness met. In conclusion, MZ generation meal kit consumers have a high rate of pursuing rational consumption. This study is valuable in understanding the priorities of the MZ generation's meal kit purchasing attributes and examining lifestyle type's purchasing behaviors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.287-297
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• 2021

The goal of this study is to investigate and provide information to the Fintech Industry on the main factors responsible for customer loyalty via the mediating effect of customer satisfaction. Secondly, providing traditional banking reasons for customer shifts from banking to Fintech, therefore these factors could be more focused. The consumer choices presented in this study can thus serve as a foundation for further research into post-adoption behaviors associated with Fintech for cross-border payments. This study examines consumer evaluations of how key attributes of fintech using mobile payment services affect their choice by using a conjoint analysis approach, which allows for the approximation of user preferences for specific features. In our study we have used SPSS 26 to test the reliability and mediation effect on the sample size of 348 people who regularly used Fintech for cross border payments. All the questionnaires were prepared if the customers were given fintech as an option instead of traditional banks to send their remittances abroad. The result shows that(Service Quality, Customer's trust and product quality) effecting customer satisfaction significantly would be very helpful for the current fintechs working for home remittances to improve these factors and would serve as a benchmark for the upcoming fintech startups and traditional banks to focus on these factors and catch up the fintech Industry. Finally, it is argued that, in order to be successful, focusing on service quality, customer trust, and product quality triggered customer loyalty for the Fintech in comparison to other traditional options for cross-border payments via the mediation effect of customer satisfaction.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.131-138
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• 2019

The film enters the color film from black and white, and the screen image changes qualitatively. The color in the film not only has the reproduction function of the restoration object like the objective appearance, but also has the function of conveying different subjective emotions. It can express the color and can't express it. The artistic effect conveys the information content that the story itself can't convey, so the color of the film becomes an important part of the film language. The color in the film is presented on the screen in the form of single-screen color, scene color, full-color color tone, and various color chains designed according to different contradictions and conflicts. Because the film art and art means are assembled by montage, he colors in the picture also form a montage form. People call it "color montage". People's subjective nature of color criticism and acceptance of color language also depend on various local tones. The accurate expression of the relationship, the unique attribute of color determines that the color must enter the structural state in order to express its unique charm. The color of the film only has the real aesthetic value when it enters the level of "color structure". This paper studies the color of Chinese and Western films from the differences between the color thinking of Chinese and Western film directors and the cultural implication of Chinese and Western film colors. The western film director emphasizes the structure of color and pays attention to the use of tonal montage to convey the characters. Emotions reflect the characteristics of a subjective color. Beginning with the "fifth-generation" director of Chinese film, the new journey of film color language has been opened. In the process of blending love and scenery, the film style of "image-in-one" has been achieved.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.791-799
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• 2008

To build the system which has high utilization and usefulness for users, it is necessary to know the information type and use-demand that the use want. The purpose of this study is to forecast the preference and demand of utilization for bus information when bus information is offered through cellular phon. The accomplishments of this research are as follow : Firstly, importance on the level of individual factor and the value of change's figure can be evaluated, using preference analysis on bus information by conjoint analysis. Secondly, by establishing the use-demand model bus information using binary logit model, influence factor on whether or not the use of the user. Finally, ordered probit model was built by use behavior model in payment per call or per month of potential user of bus information. Through call times and sensitive analysis by payment methods, elasticity point, optimal payment fee, and use probability was analyzed. This study make application as basic to efficient bus information policy and to improve use rate of bus information in future because this study make it possible to get preference analysis, use-demand analysis and estimation of optimal payment fee which is reflecting various requirement in use of bus information user.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Information Systems Review
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• v.25 no.4
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• pp.47-65
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• 2023

In this paper, we investigate how the online voting system can be a trust-based system from a technical perspective. Under four principles of voting, we finely evaluate the existing belief that offline voting is safer and more reliable than online voting based on procedural processes, technical principles. Many studies have suggested the ideas for implementing online voting system, but they have not attempted to strictly examine the technologies of online voting system from the perspective of voting requirements, and usually verification has been insufficient in terms of practical acceptance. Therefore, this study aims to analyze how the technologies are utilized to meet the demanding requirements of voting based on the technologies proven in the field. In addition to general data encryption, online voting requires more technologies for preventing data manipulation and verifying voting results. Moreover, high degree of confidentiality is required because voting data should not be exposed not only to outsiders but also to managers or the system itself. To this end, the security techniques such as Blind Signature, Bit Delegation and Key Division are used. In the case of blockchain-based voting, Mixnet and Zero-Knowledge Proof are required to ensure anonymity. In this study, the current status of the online voting system is analyzed based on the field system that actually serves. This study will enhance our understanding on online voting security technologies and contribute to build a more trust-based voting mechanism.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1111-1123
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• 2023

In recent times, semantic image segmentation methods using deep learning models have been widely used for monitoring changes in surface attributes using remote sensing imagery. To enhance the performance of various UNet-based deep learning models, including the prominent UNet model, it is imperative to have a sufficiently large training dataset. However, enlarging the training dataset not only escalates the hardware requirements for processing but also significantly increases the time required for training. To address these issues, transfer learning is used as an effective approach, enabling performance improvement of models even in the absence of massive training datasets. In this paper we present three transfer learning models, UNet-ResNet50, UNet-VGG19, and CBAM-DRUNet-VGG19, which are combined with the representative pretrained models of VGG19 model and ResNet50 model. We applied these models to building extraction tasks and analyzed the accuracy improvements resulting from the application of transfer learning. Considering the substantial impact of learning rate on the performance of deep learning models, we also analyzed performance variations of each model based on different learning rate settings. We employed three datasets, namely Kompsat-3A dataset, WHU dataset, and INRIA dataset for evaluating the performance of building extraction results. The average accuracy improvements for the three dataset types, in comparison to the UNet model, were 5.1% for the UNet-ResNet50 model, while both UNet-VGG19 and CBAM-DRUNet-VGG19 models achieved a 7.2% improvement.

• Journal of Industrial Convergence
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• v.21 no.11
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• pp.29-41
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• 2023

In this study, assuming that the maritime manned-unmanned systems, which will be used as the main force of the ROK Navy in the future, conducts its sea line of communication(SLOC) protection operations, the combat effectiveness against major threats was measured, and through this, the development direction of the manned-unmanned systems was suggested. Multi-criteria decision-making techniques such as Delphi and AHP were used to measure combat effectiveness, and the AHP survey was conducted on 40 naval officers, including 25 senior officers who are well-understood in the combat effectiveness of the weapons system and MUM-T. As an evaluation index for measuring combat effectiveness, the OODA loop was set as the main attribute, followed by Observe(0.358), Orient(0.315), Act(0.217), and Decide(0.110). The combat effectiveness of each major threat in SLOC, the lowest alternative, was measured to be 1.68 times higher than the response to maritime conflicts in neighboring countries and 3.61 times higher than the response to transnational threats. These results are expected to support rational decision-making in determining the level of technology required for acquisition of marine manned-unmanned systems and establishing operational plans for naval forces.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.